Bridging the gap between different statistical approaches: An integrated framework for modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions. The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches. This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.

Effect of changing data collection parameters on statistical motor unit number estimates

The effect of number of samples and selection of data for analysis on the calculation of surface motor unit potential (SMUP) size in the statistical method of motor unit number estimates (MUNE) was determined in 10 normal subjects and 10 with amyotrophic lateral sclerosis (ALS). We recorded 500 sequential compound muscle action potentials (CMAPs) at three different stable stimulus intensities (10–50% of maximal CMAP). Estimated mean SMUP sizes were calculated using Poisson statistical assumptions from the variance of 500 sequential CMAP obtained at each stimulus intensity. The results with the 500 data points were compared with smaller subsets from the same data set. The results using a range of 50–80% of the 500 data points were compared with the full 500. The effect of restricting analysis to data between 5–20% of the CMAP and to standard deviation limits was also assessed. No differences in mean SMUP size were found with stimulus intensity or use of different ranges of data. Consistency was improved with a greater sample number. Data within 5% of CMAP size gave both increased consistency and reduced mean SMUP size in many subjects, but excluded valid responses present at that stimulus intensity. These changes were more prominent in ALS patients in whom the presence of isolated SMUP responses was a striking difference from normal subjects. Noise, spurious data, and large SMUP limited the Poisson assumptions. When these factors are considered, consistent statistical MUNE can be calculated from a continuous sequence of data points. A 2 to 2.5 SD or 10% window are reasonable methods of limiting data for analysis. Muscle Nerve 27: 320–331, 2003

Sorting and convergence of primary olfactory axons are independent of the olfactory bulb

Primary olfactory axons expressing the same odorant receptor gene sort out and converge to fixed sites in the olfactory bulb. We examined the guidance of axons expressing the P2 odorant receptor when they were challenged with different cellular environments in vivo. In the mutant extratoes mouse, the olfactory bulb is lacking and is replaced by a fibrocellular mass. In these animals, primary olfactory axons form glomerular-like loci despite the absence of normal postsynaptic targets. P2 axons are able to sort out from other axons in this fibrocellular mass and converge to form loci of like axons. The sites of these loci along mediolateral and ventrodorsal axes were highly variable. Similar convergence was observed for larger subpopulations of axons expressing the same cell surface carbohydrates. The sorting out and convergence of like axons also occurred during regeneration following bulbectomy. Olfactory axon behaviour in these models demonstrates that sorting and convergence of axons are independent of the target, which instead provides distinct topographic cues for guidance. (C) 2003 Wiley-Liss, Inc.

The highly rearranged mitochondrial genome of the plague thrips, Thrips imaginis (Insecta : thysanoptera): Convergence of two novel gene boundaries and an extraordinary arrangement of rRNA genes

To help understand the mechanisms of gene rearrangement in the mitochondrial (mt) genomes of hemipteroid insects, we sequenced the mt genome of the plague thrips, Thrips imaginis (Thysanoptera). This genome is circular, 15,407 by long, and has many unusual features, including (1) rRNA genes inverted and distant from one another, (2) an extra gene for tRNA-Ser, (3) a tRNA-Val lacking a D-arm, (4) two pseudo-tRNA genes, (5) duplicate control regions, and (6) translocations and/or inversions of 24 of the 37 genes. The mechanism of rRNA gene transcription in T. imaginis may be different from that of other arthropods since the two rRNA genes have inverted and are distant from one another. Further, the rRNA genes are not adjacent or even close to either of the two control regions. Tandem duplication and deletion is a plausible model for the evolution of duplicate control regions and for the gene translocations, but intramitochondrial recombination may account for the gene inversions in T. imaginis. All the 18 genes between control regions #1 and #2 have translocated and/or inverted, whereas only six of the 20 genes outside this region have translocated and/or inverted. Moreover, the extra tRNA gene and the two pseudo-tRNA genes are either in this region or immediately adjacent to one of the control regions. These observations suggest that tandem duplication and deletion may be facilitated by the duplicate control regions and may have occurred a number of times in the lineage leading to T. imaginis. T. imaginis shares two novel gene boundaries with a lepidopsocid species from another order of hemipteroid insects, the Psocoptera. The evidence available suggests that these shared gene boundaries evolved by convergence and thus are not informative for the interordinal phylogeny of hemipteroid insects. We discuss the potential of hemipteroid insects as a model system for studies of the evolution of animal rut genomes and outline some fundamental questions that may be addressed with this system.

Media Convergence's Third Wave Content Streaming

Movies, pieces of music, books, or newspapers can all be expressed in the same binary code. Discrete forms of analogue media are just different dialects of the language of computerese. Content is becoming a very liquid asset. To take Marshall McLuhan's famed dictum a step further: The message is now independent of the medium

Basis set convergence in Hartree-Fock calculations of some diatomic molecules containing first and second-row atoms

Investiga-se a convergência de conjuntos de bases em direção ao limite numérico da energia Hartree-Fock (HF) total para as seqüências hierárquicas dos conjuntos de bases XZP e ccpVXZ. Para as duas hierarquias, melhoramentos significativos são obtidos com cada incremento em X. Para estimar o limite do conjunto de base completo, uma forma exponencial foi usada. Entre as várias aproximações consideradas aqui, uma extrapolação exponencial de três parâmetros aplicada aos resultados TZP, QZP e 5ZP deu os limites do conjunto de bases mais precisos. Em adição, energias HF dos orbitais moleculares ocupados mais altos de algumas moléculas diatômicas foram calculadas com o conjunto 5ZP e comparadas com as correspondentes obtidas com o conjunto cc-pV5Z e com um método numérico HF.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

The convergence of the Central American countries to International Accounting Standards

Abstract: In the last few decades, Central American countries are making a significant effort in order to modernize their governments' legislation both on financial management and systems of financial information. In this sense, these countries aim to enhance the quality of public financial information in order to improve decision-making processes, decrease the level of corruption, and keep citizens informed. In this context, the purpose of this paper is twofold. Firstly, to assess the degree of similarity of the financial information that is being developed by Central American governments with regard to the recommendations set up by Ipsas, and secondly, to analyse the efforts and the strategies that those countries are carrying out in the process of implementing those standards. To determine the differences in the information containing the annual financial statements issued by national public authorities and the recommendations set up by Ipsas we conducted a deductive content analysis. In view of the results we can say that the quality of annual financial statements presented by the countries in Central America, in comparison to the recommendations by the Ipsas concerning Ifac information, is not enough. Hence, in order to operate significant changes, it is still necessary to create new strategies for the implementation of the Ipsas.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.